3D detection and characterization of ALMA sources through deep learning Article Swipe
Michele Delli Veneri
,
Łukasz Tychoniec
,
Fabrizia Guglielmetti
,
G. Longo
,
Eric Villard
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1093/mnras/stac3314
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1093/mnras/stac3314
We present a deep learning (DL) pipeline developed for the detection and characterization of astronomical sources within simulated Atacama Large Millimeter/submillimeter Array (ALMA) data cubes. The pipeline is composed of six DL models: a convolutional autoencoder for source detection within the spatial domain of the integrated data cubes, a Recurrent Neural Network (RNN) for denoising and peak detection within the frequency domain, and four residual neural networks (ResNets) for source characterization. The combination of spatial and frequency information improves completeness while decreasing spurious signal detection. To train and test the pipeline, we developed a simulation algorithm able to generate realistic ALMA observations, i.e. both sky model and dirty cubes. The algorithm simulates always a central source surrounded by fainter ones scattered within the cube. Some sources were spatially superimposed in order to test the pipeline deblending capabilities. The detection performances of the pipeline were compared to those of other methods and significant improvements in performances were achieved. Source morphologies are detected with subpixel accuracies obtaining mean residual errors of 10−3 pixel (0.1 mas) and 10−1 mJy beam−1 on positions and flux estimations, respectively. Projection angles and flux densities are also recovered within 10 per cent of the true values for 80 and 73 per cent of all sources in the test set, respectively. While our pipeline is fine-tuned for ALMA data, the technique is applicable to other interferometric observatories, as SKA, LOFAR, VLBI, and VLTI.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/mnras/stac3314
- OA Status
- green
- Cited By
- 7
- References
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- Related Works
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- OpenAlex ID
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https://doi.org/10.1093/mnras/stac3314Digital Object Identifier
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3D detection and characterization of ALMA sources through deep learningWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-11-12Full publication date if available
- Authors
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Michele Delli Veneri, Łukasz Tychoniec, Fabrizia Guglielmetti, G. Longo, Eric VillardList of authors in order
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https://doi.org/10.1093/mnras/stac3314Publisher landing page
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2211.11462Direct OA link when available
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LOFAR, Computer science, Residual, Sky, Pipeline (software), Interferometry, Convolutional neural network, Subpixel rendering, Remote sensing, Artificial intelligence, Physics, Algorithm, Pixel, Optics, Geology, Astrophysics, Telecommunications, Programming language, Low frequencyTop concepts (fields/topics) attached by OpenAlex
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7Total citation count in OpenAlex
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2025: 2, 2024: 1, 2023: 4Per-year citation counts (last 5 years)
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| abstract_inverted_index.VLTI. | 235 |
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| abstract_inverted_index.cube. | 124 |
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